TFLite not support Dynamic input size

[WenguoLi]

System information

• TensorFlow version (you are using): 1.12
• Are you willing to contribute it (Yes/No): No

Describe the feature and the current behavior/state.

Try running tflite_convert again, with --input_shapes=1,none,none,3, but these conversion cannot be supported.
For some networks requiring dynamic input, the static input of tflite is too limited, such as Superresolution Neural Network.

[jdduke]

Can you provide a link/reference to an existing (trained) model that requires dynamic shape elements? That will help us focus support for this feature. Thanks.

[jdduke]

To add some context, support for conversion with explicitly dynamic dimensions is on our radar.

[weiweizhou]

hello,I'm trying to deploy Superresolution on android, and I found it seems not support Dynamic input size,
have you fix this problem now?
@jdduke

[jdduke]

We're actively working to improve this. Expect a change in the next few days which will improve the ability to dynamically resize at runtime. You'll still need to give it a static shape during conversion, but the graph should be much more robust to dynamic size changes.

[gnsmrky]

@jdduke Any updates in supporting dynamic size at runtime?

[jdduke]

We've landed several fixes, but they've been rolled back due to downstream clients depending on the current (somewhat broken) behavior in our converter. We're hoping to resolve this properly in Q3, and might expose an experimental flag in the converter to generate graphs that always preserve rank/shape operators.